#### purpose: producing figure F6 #### 

#### installing key packages #### 

list.of.packages = 
  c('readstata13', 'haven', 'tidyverse', 'dplyr', 'estimatr', 'texreg',
    'gridExtra', 'ggthemes', 'wCorr', 'questionr', 'xtable', 'sf', 
    'TAM', 'purrr', 'kable', 'kableExtra', 'wCorr', 'psych',
    'psychTools')
new.packages =  list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

#### libraries #### 

suppressPackageStartupMessages(
  
  {
    
    library(readstata13)    
    library(haven)
    library(tidyverse)
    library(dplyr)
    library(estimatr)
    library(texreg)
    library(gridExtra)
    library(ggthemes)
    library(wCorr)
    library(questionr)
    library(xtable)
    library(sf)
    library(TAM)
    library(purrr)
    library(kable)
    library(kableExtra)
    library(wCorr)
    library(psych)
    library(psychTools)
    library(knitr)
    
  }
  
)

#### cmps '16 --- loading datasets #### 

load(file = "cmps_lat.RData")
load(file = "cmps_wht.RData")
load(file = "cmps_blk.RData")

#### cmps '20 --- loading datasets #### 

load(file = "cmps20w_clean.RData")
load(file = "cmps20b_clean.RData")
load(file = "cmps20l_clean.RData")
# mean(cmps20w$blm_ft)
# mean(cmps20b$blm_ft)
# mean(cmps20l$blm_ft)

# quick fix for missingness 

cmps20l$cath = ifelse(is.na(cmps20l$cath), 0, cmps20l$cath)

# another quick fix on dtp

cmps20l$dtps2 = (cmps20l$dtp + cmps20l$dtp2 + cmps20l$dtp3) / 3
cmps20w$dtps2 = (cmps20w$dtp + cmps20w$dtp2 + cmps20w$dtp3) / 3
cmps20b$dtps2 = (cmps20b$dtp + cmps20b$dtp2 + cmps20b$dtp3) / 3

#### more fixes #### 


# 2016 fixes --- recode so that outcomes = 1) oppose BLM and 2) belief BLM ineffective

cmps16$supp_blm  = 1 - cmps16$supp_blm 
cmps16$blm_eff = 1 - cmps16$blm_eff 

cmps16b$supp_blm  = 1 - cmps16b$supp_blm 
cmps16b$blm_eff = 1 - cmps16b$blm_eff

cmps16w$supp_blm  = 1 - cmps16w$supp_blm 

# 2020 fixes --- recode so 1 = anti-black 

# cmps20l$blm_supports = 1 - cmps20l$blm_supports
# cmps20l$blm_eff = 1 - cmps20l$blm_eff
# cmps20l$white_hood_rank_diff = cmps20l$white_hood_rank - cmps20l$black_hood_rank
# cmps20l$dtp = 1 - cmps20l$dtp
# cmps20l$dtp3 = 1 - cmps20l$dtp3
# cmps20l$dtps = (cmps20l$dtp + cmps20l$dtp3) / 2
# cmps20l$polint = abs(cmps20l$Q29 - 4)
# cmps20l$lmhhi = log(cmps20l$mhhi + 1)
# cmps20l$lmhhi_cty = log(cmps20l$mhhi_cty + 1)
# cmps20l$dep_rate = (cmps20l$total / ((cmps20l$pfb_cty / 100) * exp(cmps20l$ltpop_cty))) * 1000
cmps16$threat = cmps16$worry
cmps16$ltpop = log(cmps16$tpop + 1)
cmps16$lmhhi = log(cmps16$mhhi + 1)

cmps20l$threat2 = cmps20l$threat2 / max(cmps20l$threat2, na.rm = TRUE)
cmps20l$threat = cmps20l$threat2

cmps16$pnc = cmps16$pnc / max(cmps16$pnc, na.rm = TRUE)
cmps16$pfb = cmps16$pfb / max(cmps16$pfb, na.rm = TRUE)
cmps20l$pnc = cmps20l$pnc / max(cmps20l$pnc, na.rm = TRUE)
cmps20l$pfb = cmps20l$pfb / max(cmps20l$pfb, na.rm = TRUE)

labs_acc1 = c(length(cmps16$acc2[cmps16$acc2 == 0]),
              length(cmps16$acc2[cmps16$acc2 == .25]),
              length(cmps16$acc2[cmps16$acc2 == .5]),
              length(cmps16$acc2[cmps16$acc2 == .75]),
              length(cmps16$acc2[cmps16$acc2 == 1]))

labs_acc2 = c(length(cmps20l$acc2[cmps20l$acc2 == 0]),
              length(cmps20l$acc2[cmps20l$acc2 == .25]),
              length(cmps20l$acc2[cmps20l$acc2 == .5]),
              length(cmps20l$acc2[cmps20l$acc2 == .75]),
              length(cmps20l$acc2[cmps20l$acc2 == 1]))


distp1 = ggplot() + 
  geom_histogram(data = cmps16, aes(x = acc2)) + 
  labs(x = "Acculturation Index", y = "Count",
       title = "A. CMPS '16") + 
  annotate("text", x = c(0, .25, .5, .75, 1),
           y = labs_acc1 + 75,
           label = labs_acc1,
           family = "serif") + 
  theme_tufte()


distp2 = ggplot() + 
  geom_histogram(data = cmps20l, aes(x = acc2)) + 
  labs(x = "Acculturation Index", y = "Count",
       title = "B. CMPS '20") + 
  annotate("text", x = c(0, .25, .5, .75, 1),
           y = labs_acc2 + 75,
           label = labs_acc2,
           family = "serif") + 
  theme_tufte()

distp_grob = arrangeGrob(distp1, distp2, ncol = 2)


ggsave(plot = distp_grob, width = 8, height = 2.5, filename = "distplotacc.png")
